Case study
Scaling Home Services HVAC Leads with Google Ads and AI Search
The Opportunity
Improving acquisition efficiency in a high-intent, high-cost local market
The client, a mid-size regional HVAC company serving three major metro areas, had an active demand generation program but an inefficient acquisition model. Google Ads was producing calls and form fills, yet the underlying economics were underwhelming. Customer acquisition cost had climbed to $412 per booked job, well above target, and management had limited visibility into which campaigns were driving profitable service calls, replacement opportunities, and closed revenue.
The issue was not demand. The issue was how the program was being measured and managed. Campaigns were optimized primarily to top-of-funnel actions such as raw calls and form submissions, while the business generated value only when those leads turned into dispatched jobs, sold installs, maintenance memberships, and high-margin replacement work. In practice, too much spend was going toward low-intent queries, weak geo targeting, and calls that never translated into appointments.
The account structure had also become fragmented, which diluted learning and reduced efficiency. Performance Max was contributing volume, but with limited transparency and weak downstream signal quality. Branded and non-branded demand were not cleanly separated. Landing pages were too generic. After-hours lead capture was inconsistent. At the same time, the company had little presence in the emerging AI-driven answer layer, where more homeowners were beginning their search for the best local HVAC provider before ever clicking a traditional ad.
The client needed to do four things at once: lower CAC, improve lead quality, scale profitable demand, and strengthen visibility across both Google Search and AI-led discovery.
The Solution
Rebuilding Google Ads around booked jobs, replacement revenue, and local market economics
We began by resetting the performance model. Rather than optimizing to call volume and form fills, we shifted the program toward downstream business outcomes: booked appointments, dispatched jobs, replacement estimate opportunities, maintenance memberships, and closed-won revenue weighted by margin. This aligned paid media more closely with how the business actually created value.
That required a substantial upgrade to the measurement infrastructure. Working across marketing, operations, dispatch, and systems, we rebuilt conversion tracking across GA4, Google Tag Manager, enhanced conversions, offline conversion imports, and CRM event capture. We connected click and call data back to outcomes in ServiceTitan, improved attribution and call visibility through CallRail and WhatConverts, and centralized reporting in BigQuery and Looker Studio. The objective was straightforward: give Google better feedback on which leads were becoming profitable HVAC jobs.
To improve precision further, we strengthened both audience activation and cross-channel measurement. Hightouch was used to push first-party customer and lifecycle signals back into paid media platforms, enabling cleaner suppression, winback, reactivation, and audience segmentation based on actual service history. Northbeam provided a broader measurement layer as the channel mix expanded beyond core search, helping the team assess blended efficiency, assisted conversion impact, and the role of upper-funnel media more accurately.
With the signal quality improved, we simplified and sharpened the account structure. The existing setup was overly fragmented by geography, keyword type, and service variation, which slowed learning and made budget allocation less effective. We consolidated around the variables that mattered most commercially: emergency repair, seasonal tune-ups, replacement and financing intent, ductless demand, and high-value service areas. Search was rebuilt with clearer segmentation between branded, competitor, and non-branded demand, tighter query control, and a more disciplined negative-keyword framework. Google Ads Editor, structured bulk workflows, and advanced Excel-based planning tools supported faster execution without reintroducing complexity.
Search remained the economic core of the program, but the broader channel mix was redesigned to improve overall efficiency. Standard Search captured high-intent repair and install demand. Dynamic Search Ads served as a controlled discovery layer. Performance Max remained in the mix, but only after the signal quality and asset governance were strong enough to support it. YouTube was added selectively to support financing awareness, replacement education, and brand reinforcement in priority markets.
We also treated AI search as an emerging local demand channel, not simply a future consideration. As more consumers began using AI-led interfaces to ask who the best local HVAC provider was, the company needed to be visible not only in paid auctions, but also in recommendation environments. We improved the company’s location pages, service pages, FAQs, financing content, technician profiles, structured data, and review-backed authority signals to increase its presence in AI-generated answers and organic local recommendation surfaces. An internal AI agent was developed and used to monitor search queries, review themes, call transcripts, and prompt patterns to identify content gaps, FAQ opportunities, negative-keyword additions, and new messaging angles. This was not about automating strategy. It was about making the system more responsive.
In parallel, we addressed post-click and post-call conversion friction. Landing pages were rebuilt by service line and metro, mobile performance improved, financing language was made more prominent, and trust signals were surfaced earlier in the journey. We also tightened call-routing logic and introduced a simple after-hours AI voice workflow that captured emergency service intent, screened basic job type, and routed viable opportunities into the next available response queue rather than losing them overnight.
Finally, we introduced a more disciplined operating cadence around growth and efficiency. Budget allocation was managed not only by platform performance, but also by weather patterns, seasonal demand, technician capacity, close rates, average ticket, and install economics by market. Reporting in Looker Studio connected spend directly to booked jobs, replacement estimates, memberships, and closed revenue. Weekly optimization combined search-term analysis, bid and budget reallocation, landing-page testing, call review, and AI-search content updates into a single operating rhythm.
The Impact
Lower CAC, better job quality, and a more scalable acquisition system
Reduction in CAC, from $412 to $209 per booked job
Increase in qualified HVAC leads, from 286 to 802 per month
Increase in monthly ad spend, from $182K to $293K, while remaining below the $225 CAC target
The improvement came from strengthening the full acquisition system, not from any single channel tactic. Once Google began optimizing against booked jobs, replacement intent, and revenue outcomes rather than low-value lead proxies, campaign performance improved materially. Once landing experiences and call handling were rebuilt around actual homeowner intent, booking rates increased. Once dispatch and media were operating from the same scorecard, the business could scale more confidently.
The gains were visible across the funnel. Paid inbound call booking rate improved 34%. Replacement estimate volume increased 2.1x. After-hours lead recovery improved 41% as routing and AI-assisted intake were tightened. Branded demand also strengthened as YouTube, reviews, and AI-search visibility reinforced local authority. Most importantly, the company moved from paying for HVAC activity to paying for HVAC outcomes.
Lessons Learned
Strategic Roadmap
Optimize to business value, not lead proxies
In HVAC, raw calls are often a poor proxy for growth. The real unlock came from aligning optimization to booked jobs, replacement opportunities, and revenue-weighted outcomes rather than top-of-funnel activity that looked efficient in-platform but converted poorly downstream.
Operating Model
Paid media performance depends on operational feedback loops
The most meaningful improvements came from better coordination across paid media, dispatch, call handling, CRM data, and financial reporting. In home services, underperformance is often not a keyword problem. It is a systems problem.
More Booked Jobs
Remove friction before increasing spend
The program did not scale simply because more budget was deployed. It scaled because the user journey improved: better query control, stronger landing experiences, clearer financing messaging, better call routing, and fewer breaks between lead capture and booked work. That made every advertising dollar more productive.
Adoption & Scaling
AI search is becoming part of local acquisition strategy
Winning local demand is no longer limited to traditional search results. Consumers are increasingly asking AI-led tools for provider recommendations before they visit a website. Companies that strengthen their authority signals, public content footprint, review base, and structured local presence will have an advantage across both Google Ads and AI-driven discovery.
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